Cardiovascular Test Methods: Part II Design Validation ... documents/Standards Activities/International... · Cardiovascular Test Methods: Part II Design Validation Testing (F2477,

Kenneth E. Perry, Ph.D.ECHOBIO LLC

Bainbridge Island, WA

PERU Workshop on Medical Device Regulation:Policy and Technical Aspects

Cardiovascular Test Methods: Part IIDesign Validation Testing (F2477, F2914)

http://echobio.com/�http://echobio.com/�

Manufacturers, regulatory agencies, physicians, engineers and patients

How can we best learn from the past and make better products?

Design->Evaluation->Market

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In general, there are four models device manufacturers use (and the FDA reviews) in the pre‐market setting to demonstrate safety and performance of a cardiovascular device:

Models Used for Evaluation


Models and Their Advantages

Computer modeling in medical devices, as compared to other industries, is nascent and there is great potential for refinement/improvement because the other models are fairly mature

Adapted from Victor Krauthamer


Safety and Effectiveness

• There is reasonable assurance that a device is safe when it can be determined, based upon valid scientific evidence, that the probable benefits to health from use of the device for its intended uses and conditions of use, outweigh any probable risks.

• There is reasonable assurance that a device is effective when it can be determined, based upon valid scientific evidence, that in a significant portion of the target population, will provide clinically significant results.


Develop risk analysis and evaluation strategy to assess thetechnical and clinical considerations for the delivery systemand the implant

Characteristics important for device evaluation: Device Functional Properties


ANIMAL MODEL– Anatomical and physiological similarities may enable someshort term safety evaluation. E.g., Deployment inaccuracies– Importantly, they provide histological information that is notreadily available in humans

Advantages of Animal Models


ANIMAL MODEL– Anatomical and physiological differences limit utility of somepre‐clinical testing– Modes of deformation (e.g. SFA) are not the same– Healing time course is accelerated compared to humans– Inferences derived may be species‐dependent

Limitations of Animal Models


BENCH‐TOP MODEL– Characterize a specific mechanical function of the device that may be difficult to examine in vivo or with a computer model– Deform the device under “worst‐case” conditions that may not be readily reproduced in the clinical setting– Understand mechanical limitations– Test the “actual” manufactured device, with material inclusions,surface treatments, residual stresses, etc.– Evaluate the initiation and nature of the fracture and wear usingimaging techniques

Advantages of Bench-top Models


Traditional fatigue testing of complete stent devices is addressed by ASTM F2477 “Standard Test Methods for in vitro Pulsatile Durability Testing of Vascular Stents,”

This standard specifies methods for fatigue of complete devices through hydrodynamic pulsation. The method involves placing complete devices into mock arteries and subjecting them to 400 million cycles of internal pressure pulsation, forcing them to radially expand and contract in each cycle.

Pulsatile Durability of Stents(ASTM F2477)


The test can either be performed between pressure limits, simulating diastolic and systolic pressures, or displacement controlled, reproducing the minimum and maximum diameters that a stent would see in vivo under worse case conditions.

F2477 has details for BOTH test methods.

In both cases, silicone tubing is carefully selected to provide the appropriate mechanical behavior.

Two Pulsatile Test Methods


Tests are typically performed at frequencies of up to 50 cycles per second, resulting in test durations of over three months, more typically at six months.

Test Speed 1 month 3 months 6 months

1 Hz 2,592,000 7,776,000 15,552,000

20 Hz 51,840,000 155,520,000 311,040,000

50 Hz 129,600,000 388,800,000 777,600,000

100 Hz 259,200,000 777,600,000 1,555,200,000

Accelerated Durability Testing


Adaptations of F2477 have been made for testing more general endovascular devices.

Application to New Device Types


Many custom tests follow the basic principles of F2477.

Application to Custom Test Methods


BENCH‐TOP MODEL– Simple loading modes and amplitudes may not replicatethe in vivo biomechanical environment– No tissue or blood interaction with the device– Limited or no incorporation of disease state (e.g., plaque or lesions)– Device not responsive to biological reactions or vessel adaptations– Limited knowledge gained if the device does not fail– Test outcomes may not predict clinical performance

Potential Limitations of the Models


COMPUTATIONAL MODEL– Examine the structural performance of an entire device familyor generations of devices under various loading conditions(whether they’re physiologic or not)– Evaluate the stress/strain distributions of a single or combinedloading modes (e.g., pulsatile and bending)– Investigate the “hot spots” or potential failure zones on thedevice– Investigate the durability of the device under a variety ofloading scenarios (safety factors!)

Some Advantages of the Models


Types of Computational Modeling

• Computational Solid Mechanics– Stents, Heart Valve Frames, Occluders, Vena Cava Filters– Spine and Joint Implants

• Computational Fluid Dynamics and Acoustics– Blood Pumps, Heart Valves, Endovascular Grafts– Drug Eluting Stents, Virus and Aerosol Transport– Ultrasound Propagation– Heat Transfer and Thermal Bioeffects

• Computational Toxicology– (Q)SAR models to predict human risk

• Computational Electromagnetics• Virtual Clinical Trials

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Current Uses of Modeling in Medical Device Applications

Stents / Heart Valve Frames / Occluders / Vena Cava Filters / AnnuloplastyRings / Dental Implants / Spine & Joint Implants / Bone Plates & Screws /

Surgical Tools

• Determine the implant size in a device family that is expected to perform the worst under simulated in vivo conditions

• Reduces the amount of physical testing• Calculate Safety Factors for static and cyclic loads

• Evaluate the effect of manufacturing tolerances• Predicate Comparison• Demonstrate a modification (e.g., dimensional) is minor and

has minimal affect on performance

Computational Solid Mechanics


Current Uses of Modeling in Medical Device ApplicationsVentricular Assist Devices / Total Artificial Heart / Blood pumps / Heart

Valves / Endovascular Grafts / Drug Eluting Devices

• Characterize the flow field by identifying regions of high shear stress, wall shear stress, or areas of low flow or flow stagnation• Especially in regions that cannot be visualized on the bench

• Determine blood damage, thrombosis potential, and drug transport using fluid flow properties

Computational Fluid Mechanics

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Passive and Active Cardiology Implants / Peripheral Implants / Joint and Spinal Implants / Deep Brain Stimulators / MR-guided Interventional Devices

• Simulate the radiofrequency energy absorbed by patients undergoing magnetic resonance imaging (MRI)

• Especially worst-case conditions that cannot be replicated in an animal model and cannot be tested ethically in humans

• Radiofrequency-induced currents and heating of (external) devices for electrophysiological recordings

• Simulate the electric/magnetic field generated by a device during use to provide evidence of effectiveness

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Computational Electromagnetism



Anesthesiology Devices / Artificial Pancreas / Neurodiagnostic Tools

• Use the simulation as an alternative validation method to demonstrate device performance and robustness

• In silico simulation model (control algorithm) of diabetes replaces in vivo animal testing for evaluating artificial pancreas

• Signal modeling (EEG source localizing software) for brain activity analysis

Physiological Closed-Loop Controllers & Algorithms



Ablation Devices

• Determine the thermal field distributions generated by tissue ablation devices (e.g., High Intensity Ultrasound, radiofrequency)

• Assess potential damage to surrounding tissue, organs and bones

Computational Thermal Mapping


COMPUTATIONAL MODEL

• Not all factors and scales can be added to the model (yet)

• Physiologically relevant boundary conditions and input data are not

always known

• Device cannot respond to biological reactions and adaptations

• Cannot accurately model complex time‐dependent materials

• Lack robust validation for biomedical applications

• May be computationally expensive to examine complex loading and

boundary conditions of an entire device family

Potential Limitations of the Models


Many issues arise (e.g., ethical) if we were to evaluate alldevices in randomized controlled, double‐blinded studies.

Therefore, in the Division of Cardiovascular Devices, we encouragethe use of the ABC Models (animal, bench, and computer) toevaluate a device

For most types of devices, we have learned much from the clinical studies to influence the nonclinical evaluation• Gain some safety information before the clinical evaluation;• Therefore, single‐arm, relatively low number of patients is acceptable.

Ideally, a comprehensive evaluation plan should utilize the advantages of the ABC models to demonstrate safety and performance of the device.

Harness the power of the ABC Models


The Bridge between Bench‐top to Bedside

• The bridge is bi‐directional• Clinical lessons greatly influence design and evaluation.• The ABC models yield safety data (especially for new or modified devices) before the clinical evaluation.

• Understanding the Anatomical and Physiological / Pathophysiological variations are crucial for design and evaluation.

• Boundary conditions for the ABC models can be furtheradvanced by clinical data and knowledge.

What have we learned about using ABC models?


Animal, Bench and Computational models can improve medical device design and increase access to safe and effective therapies for patients.

Appropriately balanced and clinically relevant conditions and the development of new tests and methodologies can push the evaluation of devices forward.

Safety and Effective Clinical Trials


How does one select durability test conditions to simulate what a cardiac implant might encounter in the body?

What to do when the implant has a complex geometry, non-standard boundary conditions with little or no precedent?

Benefits of using medical imaging data and FEA• Combine data to provide clearer picture• Interpret data based on assumptions• Evaluate, refine and develop theories

Estimating In Vivo Load Conditions


Patient imaging data is under-utilized for device design and performance validation

For many new devices and concepts, loading conditions are not known in advance

In situ Deformation of Aortic Valve


The valve frame experiences three cycles of contraction for every heart beat.

Imaging studies can quantify clinically relevant loading conditions and provide essential data and for selecting bench-top testing parameters.

Spectral Analysis of Aortic Valve Frame


This percutaneous device is used to treat mitral valve disease.

The loading conditions on the device are complex and difficult to quantify.

AEF and medical imaging were used to study these conditions and reduced them to simple engineering terms.

Ancla Proximal(Situated in GCV)

Determining Complex Loading Conditions


The implant geometry can be determined by projections the characteristics of individual frames in space.

Biplane Cine with FEA Results


Mean Strain

Alt StrainStrains are computed for elastic regions. Extended over multiple frames, mean and alternating in vivo strains may be calculated.

In vivo Fatigue Strain Estimates


A Vision for Modeling and Simulation in Device Design and Development

Digital Patients: Designers download anatomic and physiologic computer models of (dozens, hundreds, thousands, …) of patients with a given disease.Virtual Clinical Trials: New device concepts are “deployed” in digital diseased patients and performance is simulated leading to more effective bench testing, animal studies and (actual) clinical trials.

Discover “Soft Failures”Personalized Medicine: Physicians use simulation to predict safety and effectiveness of a given medical product for an individual patient.

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Computational Modeling is a powerful tool forRegulatory Decision-Making


• The ABC models yield safety data (especially for new or modified devices) before the clinical evaluation.

• Boundary conditions for the ABC models can be furtheradvanced by clinical data and knowledge

Medical imaging data and analysis is a gold mine for information about implant mechanics

Summary


ASTM F04.30.06Medical and Surgical Materials and Devices: Interventional Cardiology Task Group

Acknowledgements

Tim JohnsonMatt QuestBob Conta

Paul Bishop

Tina Morrison

Andres SchulzKasondra Totura

Ana Rosen



Cardiovascular Test Methods: Part II�Design Validation Testing (F2477, F2914) Design->Evaluation->MarketModels Used for EvaluationModels and Their AdvantagesSafety and EffectivenessCharacteristics important for device evaluation: Device Functional Properties�Advantages of Animal ModelsLimitations of Animal ModelsAdvantages of Bench-top ModelsPulsatile Durability of Stents�(ASTM F2477)Two Pulsatile Test MethodsAccelerated Durability TestingApplication to New Device TypesApplication to Custom Test Methods Potential Limitations of the ModelsSome Advantages of the ModelsTypes of Computational ModelingComputational Solid MechanicsComputational Fluid MechanicsComputational ElectromagnetismPhysiological Closed-Loop Controllers �& AlgorithmsComputational Thermal MappingPotential Limitations of the ModelsHarness the power of the ABC ModelsWhat have we learned about using �ABC models?Safety and Effective Clinical TrialsEstimating In Vivo Load ConditionsIn situ Deformation of Aortic ValveSpectral Analysis of Aortic Valve FrameDetermining Complex Loading ConditionsBiplane Cine with FEA ResultsIn vivo Fatigue Strain EstimatesA Vision for Modeling and Simulation in Device Design and DevelopmentSummaryAcknowledgementsSlide Number 36

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Cardiovascular Test Methods: Part II Design Validation ... documents/Standards Activities/International... · Cardiovascular Test Methods: Part II Design Validation Testing (F2477,